Evaluating China’s Air Pollution Control Policy with Extended AQI Indicator System: Example of the Beijing-Tianjin-Hebei Region

Analysis on Spatio-Temporal Evolution and Influencing Factors of Air Quality Index (AQI) in China

After the reform and opening up, China's economy has developed rapidly. However, environmental problems have gradually emerged, the top of which is air pollution. We have used the following methods: In view of the shortcomings of the current spatio-temporal evolution analysis of the Air Quality Index (AQI) that is not detailed to the county level and the lack of analysis of its underlying causes, this study collects the AQI of all counties in China from 2014 to 2021, and uses spatial autocorrelation and other analysis methods to deeply analyze the spatio-temporal evolution characteristic. Based on the provincial panel data, the spatial econometric model is used to explore its influencing factors and spillover effects. The research results show that: (1) From 2014 to 2021, the AQI of all counties in China showed obvious spatial agglomeration characteristics, and counties in central and western Xinjiang, as well as Beijing, Tianjin, and Hebei, were high-value agglomeration areas; (2) the change trend of the AQI value also has obvious spatial autocorrelation, and generally presents a downward trend. However, the AQI value in a small number of regions, such as Xinjiang, shows a slow decline or even a reverse rise; (3) there are some of the main factors affecting AQI, such as GDP per capita, percentage of forest cover, total emissions of SO₂, and these factors have different impacts on different regions. In addition, the increase of GDP per capita, the reduction of industrialization level, and the increase of forest coverage will significantly improve the air quality of other surrounding provinces. An in-depth analysis of the spatio-temporal evolution, influencing factors, and spillover effects of AQI in China is conducive to formulating countermeasures to improve air quality according to local conditions and promoting regional sustainable development.

The Impact of the Numbers of Monitoring Stations on the National and Regional Air Quality Assessment in China During 2013-18

China national air quality monitoring network has become the core data source for air quality assessment and management in China. However, during network construction, the significant change in numbers of monitoring sites with time is easily ignored, which brings uncertainty to air quality assessments. This study aims to analyze the impact of change in numbers of stations on national and regional air quality assessments in China during 2013-18. The results indicate that the change in numbers of stations has different impacts on fine particulate matter (PM_2.5) and ozone concentration assessments. The increasing number of sites makes the estimated national and regional PM_2.5 concentration slightly lower by 0.6-2.2 µg m^-3 and 1.4-6.0 µg m^-3 respectively from 2013 to 2018. The main reason is that over time, the monitoring network expands from the urban centers to the suburban areas with low population densities and pollutant emissions. For ozone, the increasing number of stations affects the long-term trends of the estimated concentration, especially the national trends, which changed from a slight upward trend to a downward trend in 2014-15. Besides, the impact of the increasing number of sites on ozone assessment exhibits a seasonal difference at the 0.05 significance level in that the added sites make the estimated concentration higher in winter and lower in summer. These results suggest that the change in numbers of monitoring sites is an important uncertainty factor in national and regional air quality assessments, that needs to be considered in long-term concentration assessment, trend analysis, and trend driving force analysis.

Embodied Carbon in China's Export Trade: A Multi Region Input-Output Analysis

With the rapid growth of China’s export trade and increasing pressure of domestic carbon emission reduction, the issue of carbon embodied in export trade has attracted increasing attention from academic circles. This paper has constructed a calculation model for embodied carbon in China’s export trade by using the multi-region input-output model and the international input-output data from the World Input-Output Database (WIOD) database in order to calculate the amount of embodied carbon. Our objective is to analyze the main source industry and specific sectors of embodied carbon in China’s export trade, and to provide a quantitative basis for emission reduction under the “carbon neutrality” strategy. The findings reveal that the embodied carbon in China’s export trade mainly comes from the secondary industry, which accounts for more than 90% of the total embodied carbon in export trade, while the proportions of embodied carbon in the primary industry and the tertiary industry are relatively low, about 1% and 5–7%, respectively. In terms of specific sectors, the crop and animal production and hunting sectors have the largest share (over 60%) of embodied carbon in the export trade of the primary industry; in the export trade of the secondary industry, the main sources of embodied carbon are the manufacturing sector and the power, gas, steam and air-conditioning supply sectors, respectively accounting for around 50% and 45% of the total embodied carbon in the export trade of the secondary industry; as for the tertiary industry, the transport and storage sectors have the largest share of embodied carbon in the export trade, which is around 70%. Based on the above research results, this paper has provided relevant policy recommendations, which are optimizing the export structure, improving the energy consumption structure and the carbon emissions trading system.

Location Optimisation in the Process of Designing Infrastructure of Point Pollutant Emitters to Meet Specific Environmental Protection Standards

This article addresses the challenge of searching for the optimal location for a newly designed pollutant emitter (new factory or other facility) in relation to the requirements imposed by environmental protection regulations on the concentrations of selected pollutants in a given area, taking into account the currently existing levels of analysed substances. The paper presents the key issues of the dispersion of pollutants in atmospheric air and pollutant dispersion models. The Gaussian model of a plume, based on the Pasquill diffusion equation, is chosen to simulate the dispersion of pollutants in atmospheric air. The key issue within the paper constitutes the research section responsible for using the Monte Carlo global optimisation method in order to find the optimal location. The proposed algorithm is intended to offer measurable and subjective arguments and options to preliminary discussions on choosing a location for new factories, while such discussions choices should be fact-based and ecologically acceptable instead of fulfilling only political or economical goals. The paper is intended to present the need for easily interpretable arguments for discussions and responsible decisions on choosing the lowest-impact location of pollutant emitters to the scientific community.

Examining Whistleblowing Intention: The Influence of Rationalization on Wrongdoing and Threat of Retaliation

Whistleblowers who expose wrongdoing often face several concerns, pressures, and threats of retaliation before reaching a final decision. Specifically, this study examines the effects of perceived seriousness of wrongdoing (PSW) and perceived threat of retaliation (PTR), as well as the impact of rationalization (RNL), comparing perceived seriousness of wrongdoing, perceived threat of retaliation and whistleblowing intention. Furthermore, this study aims to determine the mediating effect of anticipated regret (AR) on the relationship between perceived seriousness of wrongdoing and whistleblowing intention. We validated our model by analyzing data gathered across three stages from employees in the telecom sector in Pakistan. The key findings of our research may be summarized as follows: (i) individuals’ willingness to ‘blow the whistle’ increases as a result of perceived seriousness of wrongdoing; (ii) whistleblowers are more likely to opt to remain silent if they anticipate a greater threat of retaliation, and (iii) our study establishes a positive connection between perceived seriousness of wrongdoing and whistleblowing intention, indicating that perceived seriousness of wrongdoing enhances people’s willingness to blow the whistle, and whistleblowers are more likely to choose to emerge if the behaviour is more serious in nature; (iv) the data we have uncovered indicates a moderating role of rationalization in regulating the connections between perceived seriousness of wrongdoing, perceived threat of retaliation, and whistleblowing intention; and (v) the findings demonstrate that anticipated regret mediates the connection between perceived seriousness of wrongdoing and the intention to report wrongdoing. Additionally, the results are discussed in terms of their significance for corporate ethics researchers and managers, as well as for end-users who are interested in whistleblowing.

Regional interaction of lung cancer incidence influenced by PM2.5 in China

PM_2.5 is the key pollutant threatening human health and can even cause lung cancer. Pollution is the most serious problem in China with its fast industrialisation, urbanisation and high population density. This pollutant is conveyed through the atmosphere, trade and the embodied emission flow amongst regions. Scientific evaluation of the responsibility for regional lung cancer by considering both internal and external influences seems to be meaningful in addressing regional inequity. This study develops a relatively convenient and practical method to evaluate the regional inequity reflected by lung cancer associated with PM_2.5 pollution in China. Results show that PM_2.5 emissions and concentrations have similar distribution patterns: high values were predominant in the east and south where has high population density, while the west had low values. The cancer incidence rate showed high values mainly in eastern and central China. At a provincial scale, the lung cancer incidence rate was significantly correlated with PM_2.5 concentration levels, and a high correlation was also found between PM_2.5 concentration and emissions, indicating that emission reduction is the key to lung cancer prevention. Due to domestic trade, some developed regions more pulled lung cancer in less developed regions, and some less developed regions also have an obvious influence on external regions. Spatially, provinces in northern and central China are always more influenced by external regions. Lung cancer inequity analysis shows that coastline regions are more advantaged, while the reverse applies to inland China. The central government needs to further strengthen regional coordinated development measures, such as economic compensation for medical care and adjustments to industry structure. It should optimise spatial allocation and comprehensively consider regional inequity and character.

AQI time series prediction based on a hybrid data decomposition and echo state networks

A hybrid AQI time series prediction model is proposed based on EWT-SE-VMD secondary decomposition, ICA (imperialist competitive algorithm) feature selection, and ESN (echo state network) neural network. Firstly, EWT (empirical wavelet transform) and VMD (variational mode decomposition) are used to decompose the original AQI time series into several stable and reliable subseries. Then, the ICA is used to select features of the above subseries for the ESN prediction model. Finally, the optimized feature variables are put into the ESN deep network to establish a prediction model of each AQI subseries and obtain the future AQI index. According to the experimental results of the daily AQI series in Beijing, Tianjin, and Shijiazhuang, we find that (a) among all decomposition methods, the proposed secondary decomposition method (EWT-SE-VMD) performs best in processing data; (b) it is proved that the proposed hybrid model has broad application prospect and research value in the AQI prediction field.

Are China’s Water Resources for Agriculture Sustainable? Evidence from Hubei Province

We assessed the sustainability of agricultural water resources in Hubei Province, a typical agricultural province in central China, for a decade (2008–2018). Since traditional evaluation models often consider only the distance between the evaluation point and the positive or negative ideal solution, we introduce gray correlation analysis and construct a new sustainability evaluation model. Our research results show that only one city had excellent sustainable development capacity of agricultural water resources, and the evaluation value of eight cities fluctuated by around 0.5 (the median of the evaluation result), while the sustainable development capacity of agricultural water resources in other cities was relatively poor. Our findings not only reflect the differences in the natural conditions of water resources among various cities in Hubei, but also the impact of the cities’ policies to ensure efficient agricultural water use for sustainable development. The indicators and methods in this research are not difficult to obtain in most countries and regions of the world. Therefore, the indicator system we have established by this research could be used to study the sustainability of agricultural water resources in other countries, regions, or cities.

Analysis of the Effectiveness of Air Pollution Control Policies Based on Historical Evaluation and Deep Learning Forecast: A Case Study of Chengdu-Chongqing Region in China

Air pollution is a common problem for many countries around the world in the process of industrialization as well as a challenge to sustainable development. This paper has selected Chengdu-Chongqing region of China as the research object, which suffers from severe air pollution and has been actively involved in air pollution control in recent years to achieve sustainable development. Based on the historical data of 16 cities in this region from January 2015 to November 2019 on six major air pollutants, this paper has first conducted evaluation on the monthly air quality of these cities within the research period by using Principal Component Analysis and the Technique for Order Preference by Similarity to an Ideal Solution. Based on that, this paper has adopted the Long Short-Term Memory neural network model in deep learning to forecast the monthly air quality of various cities from December 2019 to November 2020. The aims of this paper are to enrich existing literature on air pollution control, and provide a novel scientific tool for design and formulation of air pollution control policies by innovatively integrating commonly used evaluation models and deep learning forecast methods. According to the research results, in terms of historical evaluation, the air quality of cities in the Chengdu-Chongqing region was generally moving in the same trend in the research period, with distinct characteristics of cyclicity and convergence. Year- on-year speaking, the effectiveness of air pollution control in various cities has shown a visible improvement trend. For example, Ya’an’s lowest air quality evaluation score has improved from 0.3494 in 2015 to 0.4504 in 2019; Zigong’s lowest air quality score has also risen from 0.4160 in 2015 to 0.6429 in 2019. Based on the above historical evaluation and deep learning forecast results, this paper has proposed relevant policy recommendations for air pollution control in the Chengdu-Chongqing region.

Evaluation and Treatment Analysis of Air Quality Including Particulate Pollutants: A Case Study of Shandong Province, China

At present, China’s air pollution and its treatment effect are issues of general concern in the academic circles. Based on the analysis of the development stages of air pollution in China and the development history of China’s air quality standards, we selected 17 cities of Shandong Province, China as the research objects. By expanding China’s existing Air Quality Index System, the air quality of six major pollutants including PM_2.5 and PM₁₀ in 17 cities from February 2017 to January 2020 is comprehensively evaluated. Then, with a forecast model, the air quality of the above cities in the absence of air pollution control policies since June 2018 was simulated. The results of the error test show that the model has a maximum error of 4.67% when simulating monthly assessment scores, and the maximum mean error of the four months is 3.17%. Through the comparison between the simulation results and the real evaluation results of air quality, we found that since June 2018, the air pollution control policies of six cities have achieved more than 10% improvement, while the air quality of the other 11 cities declined. The different characteristics of pollutants and the implementation of governance policies are perhaps the main reasons for the above differences. Finally, policy recommendations for the future air pollution control in Shandong and China were provided.

Air Pollution Scenario over China during COVID-19

The unprecedented slowdown in China during the COVID-19 period of November 2019 to April 2020 should have reduced pollution in smog-laden cities. However, moderate resolution imaging spectrometer (MODIS) satellite retrievals of aerosol optical depth (AOD) show a marked increase in aerosols over the Beijing–Tianjin–Hebei (BHT) region and most of Northeast and Central China, compared with the previous winter. Fine particulate (PM2.5) data from ground monitoring stations show an increase of 19.5% in Beijing during January and February 2020, and no reduction for Tianjin. In March and April 2020, a different spatial pattern emerges, with very high AOD levels observed over 50% of the Chinese mainland, and including peripheral regions in the northwest and southwest. At the same time, ozone monitoring instrument (OMI) satellite-derived NO2 concentrations fell drastically across China. The increase in PM2.5 while NO2 decreased in BTH and across China is likely due to enhanced production of secondary particulates. These are formed when reductions in NOx result in increased ozone formation, thus increasing the oxidizing capacity of the atmosphere. Support for this explanation is provided by ground level air quality data showing increased volume of fine mode aerosols throughout February and March 2020, and increased levels of PM2.5, relative humidity (RH), and ozone during haze episodes in the COVID-19 lockdown period. Backward trajectories show the origin of air masses affecting industrial centers of North and East China to be local. Other contributors to increased atmospheric particulates may include inflated industrial production in peripheral regions to compensate loss in the main population and industrial centers, and low wind speeds. Satellite monitoring of the extraordinary atmospheric conditions resulting from the COVID-19 shutdown could enhance understanding of smog formation and attempts to control it.

Analysis and Prediction of Land Use in Beijing-Tianjin-Hebei Region: A Study Based on the Improved Convolutional Neural Network Model

During the rapid economic development of China, there are certain blind decisions made in the use of land resources, which poses a significant threat to sustainable development. With the help of the improved convolutional neural network model, this paper analyzes the land use of the Beijing-Tianjin-Hebei region of China from 1995 to 2018, and provides a prediction for 2023. The research results show that: (1) There is still much room for improvement in the land use of the Beijing-Tianjin-Hebei region, with dry land taking up the largest proportion of land in these three locations; (2) Beijing’s development has been well protected in terms of land use. It is predicted that by 2023, the proportions of its woodland, grassland, and rivers, lakes, reservoirs and ponds would increase by 0.26%, 0.30%, and 0.61%, respectively, compared with their proportion in 2018; (3) the land use type in Tianjin during the research period was generally stable. In 2018, the proportion of its woodland and grassland had increased by 1.04% and 0.61%, respectively, compared with that of 1995; and (4) many ecological and environmental problems were exposed during the construction of highways in Hebei province. The area of sand land, saline-alkali land, marshland, bare land, and bare rock areas have all increased, and their total proportion is predicted to reach 1.48% by 2023.

Evaluating Regional Eco-Green Cooperative Development Based on a Heterogeneous Multi-Criteria Decision-Making Model: Example of the Yangtze River Delta Region

The coordinated development evaluation problem of a regional eco-green cooperative can be formulated as a heterogeneous multi-criteria decision-making (HMCDM) problem. Evaluation information can contain qualitative information and quantitative information, but it cannot determine the weight of the interdependence criteria subjectively. Firstly, this paper constructs an evaluation index system for regional ecological green coordinated development. The system includes four dimensions of coordinated development: the regional ecological environment, economic environment, social environment and policy environment, including 20 indicators with heterogeneous and multi-attribute characteristics. Secondly, we construct a heterogeneous multi-attribute decision-making model by calculating the relative closeness, and use the closeness to construct the value function to determine the weight index of each attribute. Finally, we make an empirical study on the performance evaluation of regional ecological green coordinated development of 27 cities in the central area of the Yangtze River Delta in China. The results show that the main driving force for the improvement of the level of regional ecological green coordinated development is the high-quality economic development, and the main obstacles to the degree of coordinated development of regional ecological green development are high energy consumption, high pollution and high emissions in industrial development; and a result found by sorting the degree of the regional ecological green coordinated development in the Yangtze River Delta city group. The heterogeneous multi-attribute decision model we proposed can effectively solve the weights of the ecological green collaborative development indicators, and the method has feasibility and effectiveness.

Evaluation of the Level of Sustainable Development of Provinces in China from 2012 to 2018: A Study Based on the Improved Entropy Coefficient-TOPSIS Method

Based on the definition and implication of sustainable development, this paper first constructed an evaluation indicator system for the sustainable development level of provinces in China, and performed a scientific evaluation on the sustainable development level based on official statistics from 2012 to 2018 by using the improved Entropy Coefficient-TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) method. The evaluation results showed that the eastern region of China has the highest level of sustainable development, with its two municipalities directly under the central government, Beijing and Shanghai, achieving the full score of 1.0000 in all evaluations, both ranking first among all the provinces. There were significant differences in the level of sustainable development across provinces in the central region, which were comparatively weaker in terms of environmental sustainability and science and technology sustainability, with four provinces’ evaluation scores below 0.5000. The provinces of the western region had comparatively lower levels of sustainable development, with six of the provinces ranking among the bottom ten in the overall sustainability score. In the northeast region, Liaoning had the highest overall sustainable development level, ranking ninth in the country, with an evaluation score of 0.7726; however, there were large differences across the region, with the other two provinces ranking 19th and 21th, respectively, in the overall sustainability score. Based on the research findings, this paper has provided relevant policy recommendations for China to further improve the sustainable development level of various provinces in the future.

Research on Air Pollution Control in China: From the Perspective of Quadrilateral Evolutionary Games

By constructing a quadrilateral evolutionary game model involving the central government, local governments, polluting enterprises, and the public, this paper attempts to comprehensively analyze the development and implementation of China’s air pollution control policies. Through the quadrilateral evolutionary game model, this paper systematically studies the evolutionary stable strategies of the four parties involved and obtains 27 equilibrium points, strategy sets, and their corresponding policy performance with the help of the four-dimensional dynamic system. The research results show that there are five equilibrium points that represent the least ideal scenarios, 14 equilibrium points that represent the less than ideal scenarios, four equilibrium points that represent the ideal scenarios, three equilibrium points that represent the more than ideal scenarios, and one equilibrium point that represents the most ideal scenarios. By analyzing the eight equilibrium points that represent the ideal, more than ideal and most ideal scenarios, especially the four stable points, this paper has obtained the conditions as well as policy implications of the four stable points in China’s air pollution control campaign.

Residents’ Willingness for Centralized Biogas Production in Hebei and Shandong Provinces

Nowadays, in the North China Plain (NCP), most counties and suburban rural regions are promoting the "coal to gas" program under the situation of strong government support of large and medium-sized centralized biogas production (CBP) projects. This paper is aimed at the CBP projects, focuses on rural residents’ “willingness to use (WTU)”, “willingness to buy (WTB)” and the influencing factors for WTU/WTB. Through the analysis of rural residents from Hebei and Shandong provinces that belong to NCP in this survey, we find that 85% of respondents support the CBP program and the WTB is 63.15 CNY/year per capita, which is much lower than the cost of rural CBP in existing survey regions. The participation of rural residents is not only a manifestation of personal and family characteristics; understanding of environmental knowledge, attitudes, and household energy practices are more important. These non-economic factors seriously affect WTU/WTB. Hence, it is necessary to appropriately increase the government revenue to such projects, strengthen government guidance and publicity, improve the environmental knowledge and attitude of rural residents and guide residents’ practice. Based on a fully understanding of residents’ consumption decision mechanism, we establish a financial support mechanism due to the demand response, adopt a correct calculation for financial support, such as a reasonable subsidy rate, and seek the greatest commonality of cost, financial support, and demand.

Analysis of Environmental Productivity on Fossil Fuel Power Plants in the U.S

In 2007, the Clean Air Act officially included greenhouse gases, making fossil fuel power plants the first of key industries regulated by the Environmental Protection Agency. How do we measure the impact of the regulations on these power plants’ productivity? Previous studies that attempt to answer this question have provided inadequate answers because their samples cover the periods only up to 2007, and they often use greenhouse gases as the only proxy for the undesirable output. This paper collects data from 133 fossil fuel power plants in the United States and covers 2004 to 2013. These power plants are divided into Sun Belt and Frost Belt based on their geographical locations. To measure the undesirable outputs, we used both carbon dioxide and toxic emissions as the proxies. The estimation model includes the construction of a generalized common stochastic frontier (metafrontier) and a Malmquist productivity index. We used the index to measure the change in productivity for the power plants before and after the implementation of the regulation. The results indicate that, since regulation in 2007, the overall production efficiency of the power plants has declined incessantly while productivity has seen a sustained downward trend despite two surges in growth.

Water Environment Management and Performance Evaluation in Central China: A Research Based on Comprehensive Evaluation System

As a developing country with insufficient water resources, China’s water environment management and performance evaluation have important research value. The three provinces (Henan, Hubei, and Hunan) in central China with typical significance in geographical location and water resources governance were selected as research objects in this paper. Based on the principal component analysis (PCA) method and the pressure-state-response (PSR) model, a comprehensive evaluation system for the water environment in those three provinces during 2011–2017 was established in this paper. The evaluation results show that: (1) The water environment management and performance evaluation of the three provinces in central China were generally poor in 2011–2012, but the overall trend was rising; (2) in 2013–2014, the situation was improved compared to the previous two years, but needed further enhancement; (3) in 2015–2017, the water environment management and performance of the three provinces showed significant improvement. Among them, the Hubei Province had the highest water environment evaluation value (1.692), and the Henan Province had the most significant progress (from 0.043 to 1.671). The contributions of this paper are: (1) The comprehensive evaluation model based on PCA and the PSR model was constructed to analyze the sustainable development of water environment in central China; (2) the performance evaluation system for water environment management, which could comprehensively evaluate the performance of water environment treatment and effectively reveal the correlation between various indicators, was established. The principal factors in water environment management can be obtained by this evaluation system. Based on the analysis of the reasons underlying the above changes, the corresponding policy recommendations for improving water environment management and performance in central China were suggested in order to provide a reference for further improvement of water environment management in developing countries.

Analysis of the Impact of China’s Hierarchical Medical System and Online Appointment Diagnosis System on the Sustainable Development of Public Health: A Case Study of Shanghai

In the study of the sustainable development of public health in China, academic circles have little to do with the unique hierarchical medical system and online appointment diagnosis system in China’s medical system. Therefore, based on the medical situation in Shanghai, China, in addition to the traditional dimension of medical expenses, this paper fully considers the impact of the current hierarchical medical policy, constructs a selection model for medical treatment behavior under the hierarchical medical system and online appointment diagnosis system, and carries out simulation analysis through the cellular automata grid dynamic model. This paper finds that the time-cost-oriented medical treatment behavior of Chinese patients will have different distribution under the current hierarchical medical system and online appointment diagnosis system. (1) When the medical treatment system neither allows online appointment nor referral, a large number of patients congregated in high-grade hospitals, with the most unreasonable distribution. (2) With the implementation of the system of allowing referral and online appointment, patients are gradually diverted to lower-grade hospitals or off-peak hours, and the distribution is relatively improved. (3) If the medical treatment system allows both referral and online appointment, the distribution of patients is the most reasonable. Therefore, China’s current hierarchical medical system and online appointment diagnosis system will, to a considerable extent, become a policy tool that affects patients’ choice of hospitals and an effective means to achieve the rational allocation of existing medical resources, which will play an important role in the sustainable development of public health in China.

Evaluation Methods of Water Environment Safety and Their Application to the Three Northeast Provinces of China

Focusing on the topic of water environment safety of China, this paper has selected the three northeast provinces of China as the research object due to their representativeness in economic development and resource security. By using the Entropy Weight Method, the Grey Correlation Analysis Method, and the Principal Component Analysis Method, this paper has first constructed a water environment safety evaluation system with 17 indicators from the economic, environmental, and ecological aspects. Furthermore, this paper has screened the initially selected indicators by the Principal Component Analysis Method and finally determined 11 indicators as the evaluation indicators. After indicator screening, this paper has adopted the improved Fuzzy Comprehensive Evaluation Method to evaluate the water environment safety of the three northeast provinces of China and obtained the change in water environment safety of different provinces from 2009 to 2017. The results show that the overall water environment safety of the region had improved first but worsened afterward, and that in terms of water safety level, Jilin Province ranked first, followed by Heilongjiang Province and Liaoning Province. The three factors that have the greatest impact on the water environment safety of the three provinces are: Liaoning—Chemical Oxygen Demand (score: 17.10), Per Capita Disposable Income (score: 13.50), and Secondary Industry Output (score: 11.50); Heilongjiang—Chemical Oxygen Demand (score: 18.64), Per Capita Water Resources (score: 12.75), and Concentration of Inhalable Particles (score: 10.89); Jilin—Per Capita Water Resources (score: 15.75), Chemical Oxygen Demand (score: 14.87), and Service Industry Output (score: 11.55). Based on analysis of the evaluation results, this paper has proposed corresponding policy recommendations to improve the water environment safety and promote sustainable development in the northeast provinces of China.

Spatiotemporal Dynamics of Ecological Total-Factor Energy Efficiency and Their Drivers in China at the Prefecture Level

Improvement of ecological total-factor energy efficiency (ETFEE) is crucial for transformation of China’s economic growth pattern, energy conservation and emissions abatement. Here we combined the epsilon-based measure (EBM) and the Global Malmquist-Luenberger (GML) productivity index to evaluate ETFEE and ecological total-factor energy productivity (ETFEP) and its decompositions for 283 prefecture-level cities in China between 2003 and 2013. A spatial econometric model is used to investigate factors influencing ETFEE and ETFEP. Results indicated that ETFEE, ETFEP and corresponding trends differ significantly depending on whether environmental constraints are considered. No convergence trend was found in ETFEE between prefecture-level cities. Technical progress plays the largest role in increasing ETFEP growth. Pure efficiency change and scale efficiency change, however, are the main hindering factors. Boosting cumulative technological progress, cumulative scale efficiency growth rate and cumulative pure efficiency growth rate are important means of increasing ETFEP. I also found that areas with high levels of economic development do not completely overlap with areas of high ETFEE. Surprisingly, the fiscal expenditure on scientific undertakings and technological spillover effects from foreign direct investment (FDI) have not substantially increased ETFEE. Whereas increased industrialization hinders the improvement of ETFEE. Furthermore, reducing per capita energy consumption help boost ETFEE. In addition, endowment advantages of factors of production have a positive overall effect on improving ETFEE. Lastly, important policy implications are inferred.

Long-Term Cointegration Relationship between China’s Wind Power Development and Carbon Emissions

Faced with the deterioration of the environment and resource shortages, countries have turned their attention to renewable energy and have actively researched and applied renewable energy. At present, a large number of studies have shown that renewable energy can effectively improve the environment and control the reduction of resources. However, there are few studies on how renewable energy improves the environment through its influencing factors. Therefore, this paper mainly analyses the relationship between wind energy and carbon emissions in renewable energy and uses Chinese data as an example for the case analysis. Based on the model and test methods, this paper uses the 1990–2018 data from the China Energy Statistical Yearbook to study and analyse the correlation between wind energy and carbon emissions and finally gives suggestions for wind energy development based on environmental improvements.

Analysis of the Air Quality and the Effect of Governance Policies in China’s Pearl River Delta, 2015–2018

The evaluation of China’s air pollution and the effectiveness of its governance policies is currently a topic of general concern in the academic community. We have improved the traditional evaluation method to construct a comprehensive air quality assessment model based on China’s major air pollutants. Using the daily air pollutant data of 2015–2018, we calculated and analyzed the monthly air quality of nine cities in the Pearl River Delta of China, and conducted a comparative study on the effect of the air pollution control policies of the cities in the Pearl River Delta. We found that the air quality control policies in those nine cities were not consistent. Specifically, the pollution control policies of Guangzhou and Foshan have achieved more than 20% improvement. The pollution control policies of Dongguan and Zhaoqing have also achieved more than 10% improvement. However, due to the relative lag of the formulation and implementation of air pollution control policies, the air quality of Jiangmen, Zhuhai and Zhongshan has declined. Based on the analysis of the air quality assessment results and the effects of governance policies in each city during the study period, we propose suggestions for further improvement of the effectiveness of air pollution control policies in the region.

Study on Development Sustainability of Atmospheric Environment in Northeast China by Rough Set and Entropy Weight Method

In order to evaluate the atmospheric environment sustainability in the provinces of Northeast China, this paper has constructed a comprehensive evaluation model based on the rough set and entropy weight methods. This paper first constructs a Pressure-State-Response (PSR) model with a pressure layer, state layer and response layer, as well as an atmospheric environment evaluation system consisting of 17 indicators. Then, this paper obtains the weight of different indicators by using the rough set method and conducts equal-width discrete analysis and clustering analysis by using SPSS software. This paper has found that different discrete methods will end up with different reduction sets and multiple indicators sharing the same weight. Therefore, this paper has further introduced the entropy weight method based on the weight solution determined by rough sets and solved the attribute reduction sets of different layers by using the Rosetta software. Finally, this paper has further proved the rationality of this evaluation model for atmospheric environment sustainability by comparing the results with those of the entropy weight method alone and those of the rough set method alone. The results show that the sustainability level of the atmospheric environment in Northeast China provinces has first improved, and then worsened, with the atmospheric environment sustainability level reaching the highest level of 0.9275 in 2014, while dropping to the lowest level of 0.6027 in 2017. Therefore, future efforts should focus on reducing the pressure layer and expanding the response layer. Based on analysis of the above evaluation results, this paper has further offered recommendations and solutions for the improvement of atmospheric environment sustainability in the three provinces of Northeast China.

Application of a Hybrid Model Based on Echo State Network and Improved Particle Swarm Optimization in PM2.5 Concentration Forecasting: A Case Study of Beijing, China

With the acceleration of urbanization, there is an increasing trend of heavy pollution. PM2.5, also known as fine particulate matter, refers to particles in the atmosphere with a diameter of less than or equal to 2.5 microns. PM2.5 has a serious impact on human life, a sustainable city, national economic development, and so on. How to forecast the PM2.5 concentration accurately, and then formulate a scientific air pollution prevention and monitoring program is of great significance. This paper proposes a hybrid model based on echo state network (ESN) and an improved particle swarm optimization (IPSO) algorithm for the Beijing air pollution problem, and provides a method for PM2.5 concentration forecasting. Firstly, the PSO algorithm is improved to speed up the search performance. Secondly, the optimal subset of the original data is selected by the convergence cross-mapping (CCM) method. Thirdly, the phase space reconstruction (PSR) process is combined with the forecasting model, and some parameters are optimized by the IPSO. Finally, the optimal variable subset is used to predict PM2.5 concentration. The 11-dimensional air quality data in Beijing from January 1 to December 31, 2016 are analyzed by the proposed method. The experimental results show that the hybrid method is superior to other comparative models in several evaluation indicators, both in one-step and multi-step forecasting of PM2.5 time series. The hybrid model has good application prospects in air quality forecasting and monitoring.

Primary Pollutants and Air Quality Analysis for Urban Air in China: Evidence from Shanghai

In recent years, China's urban air pollution has caused widespread concern in the academic world. As one of China's economic and financial centers and one of the most densely populated cities, Shanghai ranks among the top in China in terms of per capita energy consumption per unit area. Based on the Shanghai Energy Statistical Yearbook and Shanghai Air Pollution Statistics, we have systematically analyzed Shanghai’s atmospheric pollutants from three aspects: Primary pollutants, pollutants changing trends, and fine particulate matter. The comprehensive pollution index analysis method, the grey correlation analysis method, and the Euclid approach degree method are used to evaluate and analyze the air quality in Shanghai. The results have shown that Shanghai's primary pollutants are PM2.5 and O3, and the most serious air pollution happens during the first half of the year, particularly in the winter. This is because it is the peak period of industrial energy use, and residential heating will also lead to an increase in energy consumption. Furthermore, by studying the particulate pollutants of PM2.5 and PM10, we clearly disclosed the linear correlation between PM2.5 and PM10 concentrations in Shanghai which varies seasonally.

Application of Fuzzy Optimization Model Based on Entropy Weight Method in Atmospheric Quality Evaluation: A Case Study of Zhejiang Province, China

A fuzzy optimization model based on the entropy weight method for investigating air pollution problems in various cities of Zhejiang Province, China has been proposed in this paper. Meanwhile, the air quality comprehensive evaluation system has been constructed based on the six major pollutants (SO2, NO2, CO, PM10, PM2.5 and O3) involved in China’s current air quality national standards. After analyzing the monthly data of six pollutants in 11 cities of Zhejiang Province from January 2015 to April 2018 by the above method, the authors found that, although the air quality of cities in Zhejiang Province did not reach the long-term serious pollution of Beijing, Tianjin, and Hebei, the air quality changes in the northern cities of Zhejiang were worse than those in southern Zhejiang. For example, the air quality of Shaoxing in northern Zhejiang has dropped by 14.85% in the last study period when compared with that of the beginning period, and Hangzhou, the provincial capital of Zhejiang, has also seen a decrease of 6.69% in air quality. The air quality of Lishui, Zhoushan and Wenzhou in southern Zhejiang has improved by 8.04%, 4.67% and 4.22% respectively. Apart from the geographical influence, the industrial structure of these cities is also an important cause for worse air quality. From the local areas in southern Zhejiang, cities have developed targeted air pollution control measures according to their own characteristics, including adjusting the industrial structure, changing the current energy consumption structure that heavily relies on coal, and improving laws and regulations on air pollution control, etc. In the four cities in central Zhejiang, the air quality at the end of the period (April 2018) has decreased from the beginning of the period (January 2015), given that there were no fundamental changes in their industrial structure and energy pattern.